Note
to active Office without crack, just follow https://github.com/WindowsAddict/IDM-Activation-Script,
you wiil only need to run
irm https://massgrave.dev/ias | iex
from typing import Iterable | |
import itertools | |
import chess | |
pieces = [chess.Piece(p_type, c) for c in chess.COLORS for p_type in chess.PIECE_TYPES] | |
NUM_PIECES_IN_GROUP = 3 | |
groups = list(itertools.product(pieces, repeat=NUM_PIECES_IN_GROUP)) | |
BINARY_WIDTH = (len(groups) - 1).bit_length() |
{ pkgs, lib, config, ... }: with lib.types; | |
let | |
postgresqlInstanceType = submodule { | |
options.connectionString = lib.mkOption { | |
type = str; | |
}; | |
}; |
Positive Adjective List | |
abundant | |
accessible | |
accommodative | |
accomplished | |
accurate | |
achievable | |
adaptable | |
adaptive |
Note
to active Office without crack, just follow https://github.com/WindowsAddict/IDM-Activation-Script,
you wiil only need to run
irm https://massgrave.dev/ias | iex
%title: Kubeception %author: @dghubble
// Youtube: https://www.youtube.com/watch?v=tlUiQa2JYQU
-> Experiments with QEMU/KVM on Kubernetes <-
People who take my TLA+ Class get a free specification review. Cory Myers asked for a review of his Reply.tla spec, reproduced from the PR below, and has graciously agreed to let me make it public. The review itself is here.
Note this is a "light" review: I'm looking for general TLA+ antipatterns and techniques that don't require me to deeply understand the problem domain. This represents about an hour of review.
www.youtube.com###donation-shelf | |
www.youtube.com##ytd-reel-shelf-renderer.ytd-item-section-renderer.style-scope | |
www.youtube.com##+js(set, yt.config_.EXPERIMENT_FLAGS.service_worker_enabled, false) | |
www.youtube.com##+js(nano-stb, resolve(1), *, 0.001) | |
||googlevideo.com/videoplayback$xhr,3p,method=get,domain=www.youtube.com |
The dplyr
package in R makes data wrangling significantly easier.
The beauty of dplyr
is that, by design, the options available are limited.
Specifically, a set of key verbs form the core of the package.
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe.
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R.
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas
package).
dplyr is organised around six key verbs: